西安工业大学学报
西安工業大學學報
서안공업대학학보
JOURNAL OF XI'AN TECHNOLOGICAL UNIVERSITY
2014年
1期
38-43
,共6页
近红外光谱%奇异样本%免疫算法%克隆选择
近紅外光譜%奇異樣本%免疫算法%剋隆選擇
근홍외광보%기이양본%면역산법%극륭선택
near infrared spectroscopy%the singular sample%immune algorithm%clonal selection
为了提高近红外光谱数据建模后的准确性，文中提出基于免疫算法的近红外光谱奇异样本的识别方法．通过免疫算法与遗传算法对同一近红外光谱数据集分别进行奇异样本识别并比较，删除奇异样本后，免疫算法较遗传算法分别将水分、脂肪、蛋白质的PLS模型的预测误差平方和分别降低了25．8％、32．1％、21．7％．实验表明，免疫算法适用于近红外光谱奇异样本的识别，提高了模型预测精准度和稳健性．
為瞭提高近紅外光譜數據建模後的準確性，文中提齣基于免疫算法的近紅外光譜奇異樣本的識彆方法．通過免疫算法與遺傳算法對同一近紅外光譜數據集分彆進行奇異樣本識彆併比較，刪除奇異樣本後，免疫算法較遺傳算法分彆將水分、脂肪、蛋白質的PLS模型的預測誤差平方和分彆降低瞭25．8％、32．1％、21．7％．實驗錶明，免疫算法適用于近紅外光譜奇異樣本的識彆，提高瞭模型預測精準度和穩健性．
위료제고근홍외광보수거건모후적준학성，문중제출기우면역산법적근홍외광보기이양본적식별방법．통과면역산법여유전산법대동일근홍외광보수거집분별진행기이양본식별병비교，산제기이양본후，면역산법교유전산법분별장수분、지방、단백질적PLS모형적예측오차평방화분별강저료25．8％、32．1％、21．7％．실험표명，면역산법괄용우근홍외광보기이양본적식별，제고료모형예측정준도화은건성．
In order to improve the accuracy of the modeling of near infrared spectral data ,this paper presents a method for identifing the singular sample with near infrared spectroscopy by the immune algorithm .The immune algorithm and genetic algorithm are used respectively to identify the singular sample in the same NIR spectral data sets .The comparison between the results obtained by the two methods shows that ,with the singular sample removed ,the immune algorithm increases PRZSS of the PLS models of water ,fat and protein by 25 .8% ,32 .1% and 21 .7% respectively .The experimental results show that ,the artificial immune algorithm is not only suitable for the identification of the singular sample with near infrared spectra ,but also can improve the prediction accuracy and robustness .
